Papers
1–4 of 4AugVLA-3D: Depth-Driven Feature Augmentation for Vision-Language-Action Models
Vision-Language-Action (VLA) models have recently achieved remarkable progress in robotic perception and control, yet most existing approaches primarily rely on VLM trained using 2D images, which limi...
ActionCodec: What Makes for Good Action Tokenizers
Vision-Language-Action (VLA) models leveraging the native autoregressive paradigm of Vision-Language Models (VLMs) have demonstrated superior instruction-following and training efficiency. Central to ...
Chain of World: World Model Thinking in Latent Motion
Vision-Language-Action (VLA) models are a promising path toward embodied intelligence, yet they often overlook the predictive and temporal-causal structure underlying visual dynamics. World-model VLAs...
Pri4R: Learning World Dynamics for Vision-Language-Action Models with Privileged 4D Representation
Humans learn not only how their bodies move, but also how the surrounding world responds to their actions. In contrast, while recent Vision-Language-Action (VLA) models exhibit impressive semantic und...